Overview

Brought to you by YData

Dataset statistics

Number of variables34
Number of observations1941
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory515.7 KiB
Average record size in memory272.1 B

Variable types

Numeric24
Categorical10

Alerts

Edges_X_Index is highly overall correlated with LogOfAreas and 7 other fieldsHigh correlation
Edges_Y_Index is highly overall correlated with Empty_Index and 10 other fieldsHigh correlation
Empty_Index is highly overall correlated with Edges_Y_Index and 3 other fieldsHigh correlation
K_Scratch is highly overall correlated with Edges_Y_Index and 11 other fieldsHigh correlation
Length_of_Conveyer is highly overall correlated with K_ScratchHigh correlation
LogOfAreas is highly overall correlated with Edges_X_Index and 12 other fieldsHigh correlation
Log_X_Index is highly overall correlated with Edges_Y_Index and 11 other fieldsHigh correlation
Log_Y_Index is highly overall correlated with Edges_X_Index and 11 other fieldsHigh correlation
Luminosity_Index is highly overall correlated with Maximum_of_Luminosity and 1 other fieldsHigh correlation
Maximum_of_Luminosity is highly overall correlated with Luminosity_IndexHigh correlation
Minimum_of_Luminosity is highly overall correlated with K_Scratch and 6 other fieldsHigh correlation
Orientation_Index is highly overall correlated with Edges_X_Index and 4 other fieldsHigh correlation
Outside_Global_Index is highly overall correlated with Orientation_IndexHigh correlation
Outside_X_Index is highly overall correlated with Edges_Y_Index and 10 other fieldsHigh correlation
Pixels_Areas is highly overall correlated with Edges_X_Index and 10 other fieldsHigh correlation
SigmoidOfAreas is highly overall correlated with Edges_X_Index and 11 other fieldsHigh correlation
Stains is highly overall correlated with LogOfAreas and 1 other fieldsHigh correlation
Steel_Plate_Thickness is highly overall correlated with K_Scratch and 2 other fieldsHigh correlation
Sum_of_Luminosity is highly overall correlated with Edges_X_Index and 9 other fieldsHigh correlation
TypeOfSteel_A300 is highly overall correlated with Steel_Plate_Thickness and 1 other fieldsHigh correlation
TypeOfSteel_A400 is highly overall correlated with Steel_Plate_Thickness and 1 other fieldsHigh correlation
X_Maximum is highly overall correlated with K_Scratch and 1 other fieldsHigh correlation
X_Minimum is highly overall correlated with K_Scratch and 1 other fieldsHigh correlation
X_Perimeter is highly overall correlated with Edges_X_Index and 10 other fieldsHigh correlation
Y_Maximum is highly overall correlated with Y_MinimumHigh correlation
Y_Minimum is highly overall correlated with Y_MaximumHigh correlation
Y_Perimeter is highly overall correlated with Edges_X_Index and 10 other fieldsHigh correlation
Pastry is highly imbalanced (59.3%) Imbalance
Z_Scratch is highly imbalanced (53.8%) Imbalance
Stains is highly imbalanced (77.1%) Imbalance
Dirtiness is highly imbalanced (81.4%) Imbalance
X_Perimeter is highly skewed (γ1 = 21.5394512) Skewed
Y_Perimeter is highly skewed (γ1 = 39.29315841) Skewed
X_Minimum has 38 (2.0%) zeros Zeros
Edges_Index has 38 (2.0%) zeros Zeros
Orientation_Index has 91 (4.7%) zeros Zeros

Reproduction

Analysis started2025-05-21 23:21:57.145237
Analysis finished2025-05-21 23:25:35.918349
Duration3 minutes and 38.77 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

X_Minimum
Real number (ℝ)

High correlation  Zeros 

Distinct962
Distinct (%)49.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean571.13601
Minimum0
Maximum1705
Zeros38
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size15.3 KiB
2025-05-22T01:25:36.448047image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile14
Q151
median435
Q31053
95-th percentile1538
Maximum1705
Range1705
Interquartile range (IQR)1002

Descriptive statistics

Standard deviation520.69067
Coefficient of variation (CV)0.91167543
Kurtosis-1.1451435
Mean571.13601
Median Absolute Deviation (MAD)395
Skewness0.5008972
Sum1108575
Variance271118.78
MonotonicityNot monotonic
2025-05-22T01:25:37.522974image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41 126
 
6.5%
39 125
 
6.4%
0 38
 
2.0%
43 18
 
0.9%
37 12
 
0.6%
9 8
 
0.4%
2 8
 
0.4%
19 8
 
0.4%
15 7
 
0.4%
13 7
 
0.4%
Other values (952) 1584
81.6%
ValueCountFrequency (%)
0 38
2.0%
1 6
 
0.3%
2 8
 
0.4%
3 3
 
0.2%
4 4
 
0.2%
5 3
 
0.2%
6 4
 
0.2%
7 3
 
0.2%
8 3
 
0.2%
9 8
 
0.4%
ValueCountFrequency (%)
1705 1
 
0.1%
1688 1
 
0.1%
1687 2
0.1%
1685 1
 
0.1%
1683 1
 
0.1%
1682 1
 
0.1%
1680 1
 
0.1%
1678 1
 
0.1%
1677 3
0.2%
1675 1
 
0.1%

X_Maximum
Real number (ℝ)

High correlation 

Distinct994
Distinct (%)51.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean617.96445
Minimum4
Maximum1713
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.3 KiB
2025-05-22T01:25:39.604235image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile29
Q1192
median467
Q31072
95-th percentile1561
Maximum1713
Range1709
Interquartile range (IQR)880

Descriptive statistics

Standard deviation497.62741
Coefficient of variation (CV)0.80526867
Kurtosis-1.0775255
Mean617.96445
Median Absolute Deviation (MAD)349
Skewness0.52420967
Sum1199469
Variance247633.04
MonotonicityNot monotonic
2025-05-22T01:25:40.174246image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
212 23
 
1.2%
214 22
 
1.1%
218 21
 
1.1%
216 19
 
1.0%
194 14
 
0.7%
211 13
 
0.7%
192 12
 
0.6%
209 11
 
0.6%
193 10
 
0.5%
222 9
 
0.5%
Other values (984) 1787
92.1%
ValueCountFrequency (%)
4 1
 
0.1%
5 1
 
0.1%
6 1
 
0.1%
8 3
 
0.2%
9 2
 
0.1%
10 3
 
0.2%
11 3
 
0.2%
12 4
0.2%
13 5
0.3%
14 8
0.4%
ValueCountFrequency (%)
1713 1
 
0.1%
1712 1
 
0.1%
1696 1
 
0.1%
1694 2
0.1%
1692 1
 
0.1%
1690 1
 
0.1%
1689 1
 
0.1%
1688 3
0.2%
1687 2
0.1%
1686 1
 
0.1%

Y_Minimum
Real number (ℝ)

High correlation 

Distinct1939
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1650684.9
Minimum6712
Maximum12987661
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.3 KiB
2025-05-22T01:25:40.819264image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum6712
5-th percentile86773
Q1471253
median1204128
Q32183073
95-th percentile4532922
Maximum12987661
Range12980949
Interquartile range (IQR)1711820

Descriptive statistics

Standard deviation1774578.4
Coefficient of variation (CV)1.0750558
Kurtosis11.357575
Mean1650684.9
Median Absolute Deviation (MAD)817349
Skewness2.8112132
Sum3.2039793 × 109
Variance3.1491286 × 1012
MonotonicityNot monotonic
2025-05-22T01:25:41.347541image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28972 2
 
0.1%
1803992 2
 
0.1%
270900 1
 
0.1%
3411415 1
 
0.1%
173208 1
 
0.1%
252936 1
 
0.1%
33298 1
 
0.1%
197701 1
 
0.1%
244048 1
 
0.1%
266325 1
 
0.1%
Other values (1929) 1929
99.4%
ValueCountFrequency (%)
6712 1
0.1%
7003 1
0.1%
7430 1
0.1%
7851 1
0.1%
9007 1
0.1%
9228 1
0.1%
12799 1
0.1%
13302 1
0.1%
14524 1
0.1%
15184 1
0.1%
ValueCountFrequency (%)
12987661 1
0.1%
12917033 1
0.1%
12806495 1
0.1%
12725281 1
0.1%
12577343 1
0.1%
12438460 1
0.1%
12416454 1
0.1%
11741476 1
0.1%
11569824 1
0.1%
11499942 1
0.1%

Y_Maximum
Real number (ℝ)

High correlation 

Distinct1940
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1650738.7
Minimum6724
Maximum12987692
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.3 KiB
2025-05-22T01:25:41.901256image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum6724
5-th percentile86815
Q1471281
median1204136
Q32183084
95-th percentile4532948
Maximum12987692
Range12980968
Interquartile range (IQR)1711803

Descriptive statistics

Standard deviation1774590.1
Coefficient of variation (CV)1.0750279
Kurtosis11.357194
Mean1650738.7
Median Absolute Deviation (MAD)817342
Skewness2.811169
Sum3.2040838 × 109
Variance3.14917 × 1012
MonotonicityNot monotonic
2025-05-22T01:25:42.334462image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28984 2
 
0.1%
2538108 1
 
0.1%
1553931 1
 
0.1%
369415 1
 
0.1%
498335 1
 
0.1%
173226 1
 
0.1%
252958 1
 
0.1%
33323 1
 
0.1%
197718 1
 
0.1%
244067 1
 
0.1%
Other values (1930) 1930
99.4%
ValueCountFrequency (%)
6724 1
0.1%
7020 1
0.1%
7458 1
0.1%
7865 1
0.1%
9033 1
0.1%
9246 1
0.1%
12804 1
0.1%
13320 1
0.1%
14551 1
0.1%
15196 1
0.1%
ValueCountFrequency (%)
12987692 1
0.1%
12917094 1
0.1%
12806520 1
0.1%
12725314 1
0.1%
12577396 1
0.1%
12438491 1
0.1%
12416473 1
0.1%
11741833 1
0.1%
11569844 1
0.1%
11499957 1
0.1%

Pixels_Areas
Real number (ℝ)

High correlation 

Distinct920
Distinct (%)47.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1893.8784
Minimum2
Maximum152655
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.3 KiB
2025-05-22T01:25:42.699562image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile39
Q184
median174
Q3822
95-th percentile11211
Maximum152655
Range152653
Interquartile range (IQR)738

Descriptive statistics

Standard deviation5168.4596
Coefficient of variation (CV)2.7290345
Kurtosis375.8382
Mean1893.8784
Median Absolute Deviation (MAD)114
Skewness14.083822
Sum3676018
Variance26712974
MonotonicityNot monotonic
2025-05-22T01:25:43.027511image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
68 19
 
1.0%
52 19
 
1.0%
60 18
 
0.9%
55 16
 
0.8%
16 15
 
0.8%
51 15
 
0.8%
56 14
 
0.7%
110 14
 
0.7%
67 14
 
0.7%
54 14
 
0.7%
Other values (910) 1783
91.9%
ValueCountFrequency (%)
2 2
 
0.1%
6 2
 
0.1%
8 2
 
0.1%
9 2
 
0.1%
10 1
 
0.1%
11 1
 
0.1%
12 10
0.5%
14 1
 
0.1%
15 3
 
0.2%
16 15
0.8%
ValueCountFrequency (%)
152655 1
0.1%
37334 1
0.1%
25473 1
0.1%
25323 1
0.1%
24365 1
0.1%
22554 1
0.1%
21987 1
0.1%
21110 1
0.1%
21036 1
0.1%
20894 1
0.1%

X_Perimeter
Real number (ℝ)

High correlation  Skewed 

Distinct399
Distinct (%)20.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean111.85523
Minimum2
Maximum10449
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.3 KiB
2025-05-22T01:25:43.345663image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile9
Q115
median26
Q384
95-th percentile616
Maximum10449
Range10447
Interquartile range (IQR)69

Descriptive statistics

Standard deviation301.20919
Coefficient of variation (CV)2.6928485
Kurtosis715.95655
Mean111.85523
Median Absolute Deviation (MAD)15
Skewness21.539451
Sum217111
Variance90726.974
MonotonicityNot monotonic
2025-05-22T01:25:43.663508image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12 81
 
4.2%
15 75
 
3.9%
13 71
 
3.7%
14 69
 
3.6%
11 58
 
3.0%
16 57
 
2.9%
10 55
 
2.8%
9 52
 
2.7%
18 51
 
2.6%
17 51
 
2.6%
Other values (389) 1321
68.1%
ValueCountFrequency (%)
2 2
 
0.1%
3 2
 
0.1%
4 6
 
0.3%
5 10
 
0.5%
6 18
 
0.9%
7 14
 
0.7%
8 28
1.4%
9 52
2.7%
10 55
2.8%
11 58
3.0%
ValueCountFrequency (%)
10449 1
0.1%
1275 1
0.1%
1193 1
0.1%
1169 1
0.1%
1138 1
0.1%
1084 1
0.1%
1050 1
0.1%
1022 1
0.1%
1021 1
0.1%
1015 1
0.1%

Y_Perimeter
Real number (ℝ)

High correlation  Skewed 

Distinct317
Distinct (%)16.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean82.965997
Minimum1
Maximum18152
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.3 KiB
2025-05-22T01:25:43.932053image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7
Q113
median25
Q383
95-th percentile381
Maximum18152
Range18151
Interquartile range (IQR)70

Descriptive statistics

Standard deviation426.48288
Coefficient of variation (CV)5.1404539
Kurtosis1663.0518
Mean82.965997
Median Absolute Deviation (MAD)14
Skewness39.293158
Sum161037
Variance181887.65
MonotonicityNot monotonic
2025-05-22T01:25:44.238007image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11 87
 
4.5%
12 78
 
4.0%
10 72
 
3.7%
13 61
 
3.1%
14 60
 
3.1%
17 55
 
2.8%
15 54
 
2.8%
20 45
 
2.3%
8 44
 
2.3%
16 43
 
2.2%
Other values (307) 1342
69.1%
ValueCountFrequency (%)
1 2
 
0.1%
2 2
 
0.1%
3 9
 
0.5%
4 35
1.8%
5 17
 
0.9%
6 19
 
1.0%
7 35
1.8%
8 44
2.3%
9 35
1.8%
10 72
3.7%
ValueCountFrequency (%)
18152 1
0.1%
903 1
0.1%
712 1
0.1%
709 1
0.1%
696 1
0.1%
684 1
0.1%
680 1
0.1%
605 1
0.1%
604 1
0.1%
597 1
0.1%

Sum_of_Luminosity
Real number (ℝ)

High correlation 

Distinct1909
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean206312.15
Minimum250
Maximum11591414
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.3 KiB
2025-05-22T01:25:44.522366image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum250
5-th percentile4347
Q19522
median19202
Q383011
95-th percentile1293558
Maximum11591414
Range11591164
Interquartile range (IQR)73489

Descriptive statistics

Standard deviation512293.59
Coefficient of variation (CV)2.4830995
Kurtosis131.49526
Mean206312.15
Median Absolute Deviation (MAD)12505
Skewness7.73072
Sum4.0045188 × 108
Variance2.6244472 × 1011
MonotonicityNot monotonic
2025-05-22T01:25:44.838934image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8140 2
 
0.1%
9247 2
 
0.1%
13352 2
 
0.1%
8602 2
 
0.1%
7145 2
 
0.1%
29002 2
 
0.1%
7164 2
 
0.1%
6216 2
 
0.1%
13351 2
 
0.1%
7446 2
 
0.1%
Other values (1899) 1921
99.0%
ValueCountFrequency (%)
250 1
0.1%
255 1
0.1%
718 1
0.1%
764 1
0.1%
775 1
0.1%
950 1
0.1%
958 1
0.1%
1059 1
0.1%
1063 1
0.1%
1233 1
0.1%
ValueCountFrequency (%)
11591414 1
0.1%
3918209 1
0.1%
3061597 1
0.1%
3037459 1
0.1%
2935414 1
0.1%
2712104 1
0.1%
2638402 1
0.1%
2554885 1
0.1%
2529140 1
0.1%
2519511 1
0.1%

Minimum_of_Luminosity
Real number (ℝ)

High correlation 

Distinct161
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84.548686
Minimum0
Maximum203
Zeros4
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size15.3 KiB
2025-05-22T01:25:45.119854image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile29
Q163
median90
Q3106
95-th percentile124
Maximum203
Range203
Interquartile range (IQR)43

Descriptive statistics

Standard deviation32.134276
Coefficient of variation (CV)0.3800683
Kurtosis0.11237035
Mean84.548686
Median Absolute Deviation (MAD)20
Skewness-0.10709776
Sum164109
Variance1032.6117
MonotonicityNot monotonic
2025-05-22T01:25:45.434131image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
101 39
 
2.0%
97 38
 
2.0%
91 38
 
2.0%
104 37
 
1.9%
96 37
 
1.9%
99 36
 
1.9%
95 36
 
1.9%
84 35
 
1.8%
105 34
 
1.8%
77 33
 
1.7%
Other values (151) 1578
81.3%
ValueCountFrequency (%)
0 4
0.2%
4 1
 
0.1%
6 2
0.1%
7 1
 
0.1%
9 1
 
0.1%
11 2
0.1%
12 1
 
0.1%
14 1
 
0.1%
15 1
 
0.1%
16 2
0.1%
ValueCountFrequency (%)
203 1
 
0.1%
196 1
 
0.1%
195 2
0.1%
192 2
0.1%
191 1
 
0.1%
190 1
 
0.1%
179 2
0.1%
178 4
0.2%
177 1
 
0.1%
175 1
 
0.1%

Maximum_of_Luminosity
Real number (ℝ)

High correlation 

Distinct100
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean130.19371
Minimum37
Maximum253
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.3 KiB
2025-05-22T01:25:45.724115image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum37
5-th percentile102
Q1124
median127
Q3140
95-th percentile156
Maximum253
Range216
Interquartile range (IQR)16

Descriptive statistics

Standard deviation18.690992
Coefficient of variation (CV)0.14356294
Kurtosis7.8584205
Mean130.19371
Median Absolute Deviation (MAD)8
Skewness1.2870354
Sum252706
Variance349.35318
MonotonicityNot monotonic
2025-05-22T01:25:45.991483image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127 194
 
10.0%
126 151
 
7.8%
124 146
 
7.5%
141 129
 
6.6%
125 112
 
5.8%
132 109
 
5.6%
143 97
 
5.0%
134 95
 
4.9%
140 95
 
4.9%
135 88
 
4.5%
Other values (90) 725
37.4%
ValueCountFrequency (%)
37 1
 
0.1%
39 1
 
0.1%
70 1
 
0.1%
71 3
 
0.2%
77 2
 
0.1%
78 3
 
0.2%
79 2
 
0.1%
82 1
 
0.1%
84 9
0.5%
85 1
 
0.1%
ValueCountFrequency (%)
253 1
 
0.1%
252 2
 
0.1%
247 1
 
0.1%
236 1
 
0.1%
221 1
 
0.1%
220 1
 
0.1%
213 1
 
0.1%
212 3
0.2%
210 1
 
0.1%
207 5
0.3%

Length_of_Conveyer
Real number (ℝ)

High correlation 

Distinct84
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1459.1602
Minimum1227
Maximum1794
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.3 KiB
2025-05-22T01:25:46.242305image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1227
5-th percentile1353
Q11358
median1364
Q31650
95-th percentile1692
Maximum1794
Range567
Interquartile range (IQR)292

Descriptive statistics

Standard deviation144.57782
Coefficient of variation (CV)0.099082898
Kurtosis-1.1917137
Mean1459.1602
Median Absolute Deviation (MAD)11
Skewness0.85142225
Sum2832230
Variance20902.747
MonotonicityNot monotonic
2025-05-22T01:25:46.506515image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1358 242
 
12.5%
1356 186
 
9.6%
1360 156
 
8.0%
1362 107
 
5.5%
1364 101
 
5.2%
1692 94
 
4.8%
1353 85
 
4.4%
1687 84
 
4.3%
1354 81
 
4.2%
1387 68
 
3.5%
Other values (74) 737
38.0%
ValueCountFrequency (%)
1227 3
0.2%
1280 1
 
0.1%
1306 4
0.2%
1308 2
0.1%
1320 1
 
0.1%
1322 3
0.2%
1324 1
 
0.1%
1333 2
0.1%
1336 1
 
0.1%
1346 2
0.1%
ValueCountFrequency (%)
1794 1
 
0.1%
1715 2
 
0.1%
1712 1
 
0.1%
1710 6
 
0.3%
1708 3
 
0.2%
1707 1
 
0.1%
1700 2
 
0.1%
1698 19
1.0%
1696 21
1.1%
1694 36
1.9%

TypeOfSteel_A300
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.3 KiB
0
1164 
1
777 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1941
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1164
60.0%
1 777
40.0%

Length

2025-05-22T01:25:46.752401image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-22T01:25:46.949747image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
0 1164
60.0%
1 777
40.0%

Most occurring characters

ValueCountFrequency (%)
0 1164
60.0%
1 777
40.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1941
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1164
60.0%
1 777
40.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1941
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1164
60.0%
1 777
40.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1941
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1164
60.0%
1 777
40.0%

TypeOfSteel_A400
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.3 KiB
1
1164 
0
777 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1941
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1164
60.0%
0 777
40.0%

Length

2025-05-22T01:25:47.153096image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-22T01:25:47.328931image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
1 1164
60.0%
0 777
40.0%

Most occurring characters

ValueCountFrequency (%)
1 1164
60.0%
0 777
40.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1941
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 1164
60.0%
0 777
40.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1941
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 1164
60.0%
0 777
40.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1941
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 1164
60.0%
0 777
40.0%

Steel_Plate_Thickness
Real number (ℝ)

High correlation 

Distinct24
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean78.737764
Minimum40
Maximum300
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.3 KiB
2025-05-22T01:25:47.523720image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum40
5-th percentile40
Q140
median70
Q380
95-th percentile200
Maximum300
Range260
Interquartile range (IQR)40

Descriptive statistics

Standard deviation55.086032
Coefficient of variation (CV)0.69961387
Kurtosis4.9378385
Mean78.737764
Median Absolute Deviation (MAD)30
Skewness2.2069351
Sum152830
Variance3034.4709
MonotonicityNot monotonic
2025-05-22T01:25:47.758147image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
40 710
36.6%
70 380
19.6%
100 154
 
7.9%
80 150
 
7.7%
50 115
 
5.9%
60 87
 
4.5%
200 79
 
4.1%
300 43
 
2.2%
69 39
 
2.0%
175 33
 
1.7%
Other values (14) 151
 
7.8%
ValueCountFrequency (%)
40 710
36.6%
50 115
 
5.9%
60 87
 
4.5%
69 39
 
2.0%
70 380
19.6%
80 150
 
7.7%
85 4
 
0.2%
90 23
 
1.2%
100 154
 
7.9%
120 25
 
1.3%
ValueCountFrequency (%)
300 43
2.2%
290 2
 
0.1%
250 2
 
0.1%
220 16
 
0.8%
211 5
 
0.3%
200 79
4.1%
185 14
 
0.7%
180 2
 
0.1%
175 33
1.7%
150 26
 
1.3%

Edges_Index
Real number (ℝ)

Zeros 

Distinct1387
Distinct (%)71.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3317152
Minimum0
Maximum0.9952
Zeros38
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size15.3 KiB
2025-05-22T01:25:48.010567image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0118
Q10.0604
median0.2273
Q30.5738
95-th percentile0.9029
Maximum0.9952
Range0.9952
Interquartile range (IQR)0.5134

Descriptive statistics

Standard deviation0.29971175
Coefficient of variation (CV)0.9035213
Kurtosis-0.90420928
Mean0.3317152
Median Absolute Deviation (MAD)0.1742
Skewness0.68577108
Sum643.8592
Variance0.089827132
MonotonicityNot monotonic
2025-05-22T01:25:48.286928image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0604 43
 
2.2%
0 38
 
2.0%
0.0574 34
 
1.8%
0.0557 30
 
1.5%
0.0585 25
 
1.3%
0.0605 24
 
1.2%
0.0586 21
 
1.1%
0.0556 21
 
1.1%
0.0575 20
 
1.0%
0.0558 12
 
0.6%
Other values (1377) 1673
86.2%
ValueCountFrequency (%)
0 38
2.0%
0.0012 2
 
0.1%
0.0014 2
 
0.1%
0.0015 4
 
0.2%
0.0023 1
 
0.1%
0.0024 8
 
0.4%
0.003 3
 
0.2%
0.0035 1
 
0.1%
0.0036 2
 
0.1%
0.0043 1
 
0.1%
ValueCountFrequency (%)
0.9952 1
 
0.1%
0.9923 1
 
0.1%
0.9905 1
 
0.1%
0.9897 1
 
0.1%
0.9846 2
0.1%
0.9835 1
 
0.1%
0.9834 1
 
0.1%
0.9816 1
 
0.1%
0.9808 3
0.2%
0.9795 1
 
0.1%

Empty_Index
Real number (ℝ)

High correlation 

Distinct1338
Distinct (%)68.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.41420335
Minimum0
Maximum0.9439
Zeros2
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size15.3 KiB
2025-05-22T01:25:48.560305image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.2083
Q10.3158
median0.4121
Q30.5016
95-th percentile0.6448
Maximum0.9439
Range0.9439
Interquartile range (IQR)0.1858

Descriptive statistics

Standard deviation0.13726149
Coefficient of variation (CV)0.33138672
Kurtosis0.18930051
Mean0.41420335
Median Absolute Deviation (MAD)0.0929
Skewness0.29346776
Sum803.9687
Variance0.018840716
MonotonicityNot monotonic
2025-05-22T01:25:48.838939image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3333 28
 
1.4%
0.25 21
 
1.1%
0.3636 12
 
0.6%
0.2222 12
 
0.6%
0.2 12
 
0.6%
0.4 11
 
0.6%
0.375 11
 
0.6%
0.3 10
 
0.5%
0.5 10
 
0.5%
0.2778 9
 
0.5%
Other values (1328) 1805
93.0%
ValueCountFrequency (%)
0 2
0.1%
0.0278 1
0.1%
0.0368 1
0.1%
0.0595 1
0.1%
0.0682 1
0.1%
0.0714 1
0.1%
0.0781 1
0.1%
0.0818 1
0.1%
0.0926 1
0.1%
0.0972 1
0.1%
ValueCountFrequency (%)
0.9439 1
0.1%
0.9275 1
0.1%
0.894 1
0.1%
0.8888 1
0.1%
0.8856 1
0.1%
0.8817 1
0.1%
0.8767 1
0.1%
0.8648 1
0.1%
0.8487 1
0.1%
0.8473 1
0.1%

Square_Index
Real number (ℝ)

Distinct770
Distinct (%)39.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.57076713
Minimum0.0083
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.3 KiB
2025-05-22T01:25:49.108114image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0.0083
5-th percentile0.125
Q10.3613
median0.5556
Q30.8182
95-th percentile0.9912
Maximum1
Range0.9917
Interquartile range (IQR)0.4569

Descriptive statistics

Standard deviation0.27105839
Coefficient of variation (CV)0.47490188
Kurtosis-1.1580304
Mean0.57076713
Median Absolute Deviation (MAD)0.2223
Skewness-0.056305677
Sum1107.859
Variance0.073472648
MonotonicityNot monotonic
2025-05-22T01:25:49.383033image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 91
 
4.7%
0.8 42
 
2.2%
0.5 38
 
2.0%
0.6667 37
 
1.9%
0.3333 31
 
1.6%
0.75 30
 
1.5%
0.8889 28
 
1.4%
0.9091 27
 
1.4%
0.8571 25
 
1.3%
0.4 24
 
1.2%
Other values (760) 1568
80.8%
ValueCountFrequency (%)
0.0083 1
0.1%
0.009 1
0.1%
0.0261 1
0.1%
0.0294 1
0.1%
0.0393 1
0.1%
0.0396 1
0.1%
0.0408 1
0.1%
0.0422 1
0.1%
0.0441 1
0.1%
0.0448 1
0.1%
ValueCountFrequency (%)
1 91
4.7%
0.9955 1
 
0.1%
0.9945 1
 
0.1%
0.9942 3
 
0.2%
0.993 1
 
0.1%
0.9912 1
 
0.1%
0.9895 1
 
0.1%
0.9884 1
 
0.1%
0.9867 1
 
0.1%
0.9832 1
 
0.1%

Outside_X_Index
Real number (ℝ)

High correlation 

Distinct454
Distinct (%)23.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.033361103
Minimum0.0015
Maximum0.8759
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.3 KiB
2025-05-22T01:25:49.709229image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0.0015
5-th percentile0.0044
Q10.0066
median0.0101
Q30.0235
95-th percentile0.1289
Maximum0.8759
Range0.8744
Interquartile range (IQR)0.0169

Descriptive statistics

Standard deviation0.058961169
Coefficient of variation (CV)1.7673627
Kurtosis46.109128
Mean0.033361103
Median Absolute Deviation (MAD)0.0047
Skewness5.1818301
Sum64.7539
Variance0.0034764195
MonotonicityNot monotonic
2025-05-22T01:25:49.997071image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0059 75
 
3.9%
0.0066 71
 
3.7%
0.0081 53
 
2.7%
0.0088 48
 
2.5%
0.0053 46
 
2.4%
0.0074 45
 
2.3%
0.0044 42
 
2.2%
0.0047 42
 
2.2%
0.0052 40
 
2.1%
0.0065 40
 
2.1%
Other values (444) 1439
74.1%
ValueCountFrequency (%)
0.0015 2
 
0.1%
0.0022 3
 
0.2%
0.0024 1
 
0.1%
0.0029 4
 
0.2%
0.003 6
 
0.3%
0.0035 5
 
0.3%
0.0036 8
 
0.4%
0.0037 29
1.5%
0.0041 20
1.0%
0.0042 2
 
0.1%
ValueCountFrequency (%)
0.8759 1
0.1%
0.6226 1
0.1%
0.6209 1
0.1%
0.5906 1
0.1%
0.5692 1
0.1%
0.4964 1
0.1%
0.4957 1
0.1%
0.4698 1
0.1%
0.4177 1
0.1%
0.3878 1
0.1%

Edges_X_Index
Real number (ℝ)

High correlation 

Distinct818
Distinct (%)42.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.61052865
Minimum0.0144
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.3 KiB
2025-05-22T01:25:50.265960image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0.0144
5-th percentile0.2105
Q10.4118
median0.6364
Q30.8
95-th percentile1
Maximum1
Range0.9856
Interquartile range (IQR)0.3882

Descriptive statistics

Standard deviation0.24327692
Coefficient of variation (CV)0.3984693
Kurtosis-0.9302592
Mean0.61052865
Median Absolute Deviation (MAD)0.1819
Skewness-0.23509587
Sum1185.0361
Variance0.059183659
MonotonicityNot monotonic
2025-05-22T01:25:50.565683image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 135
 
7.0%
0.8 49
 
2.5%
0.75 43
 
2.2%
0.6667 41
 
2.1%
0.5 33
 
1.7%
0.8333 24
 
1.2%
0.8571 21
 
1.1%
0.9 21
 
1.1%
0.8889 20
 
1.0%
0.7778 20
 
1.0%
Other values (808) 1534
79.0%
ValueCountFrequency (%)
0.0144 1
0.1%
0.0645 1
0.1%
0.0657 1
0.1%
0.0717 1
0.1%
0.0724 1
0.1%
0.0782 1
0.1%
0.0794 1
0.1%
0.0874 1
0.1%
0.0909 1
0.1%
0.0968 1
0.1%
ValueCountFrequency (%)
1 135
7.0%
0.9965 1
 
0.1%
0.9879 1
 
0.1%
0.9828 1
 
0.1%
0.9804 1
 
0.1%
0.9776 1
 
0.1%
0.975 1
 
0.1%
0.9688 2
 
0.1%
0.9682 1
 
0.1%
0.9671 1
 
0.1%

Edges_Y_Index
Real number (ℝ)

High correlation 

Distinct648
Distinct (%)33.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.81347223
Minimum0.0484
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.3 KiB
2025-05-22T01:25:50.826970image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0.0484
5-th percentile0.381
Q10.5968
median0.9474
Q31
95-th percentile1
Maximum1
Range0.9516
Interquartile range (IQR)0.4032

Descriptive statistics

Standard deviation0.23427362
Coefficient of variation (CV)0.28799216
Kurtosis-0.56319361
Mean0.81347223
Median Absolute Deviation (MAD)0.0526
Skewness-0.92858241
Sum1578.9496
Variance0.05488413
MonotonicityNot monotonic
2025-05-22T01:25:51.083700image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 820
42.2%
0.6667 19
 
1.0%
0.75 16
 
0.8%
0.9231 16
 
0.8%
0.8333 16
 
0.8%
0.8 14
 
0.7%
0.9091 14
 
0.7%
0.9167 13
 
0.7%
0.9 12
 
0.6%
0.875 12
 
0.6%
Other values (638) 989
51.0%
ValueCountFrequency (%)
0.0484 1
0.1%
0.105 1
0.1%
0.1123 1
0.1%
0.1312 1
0.1%
0.1321 1
0.1%
0.1378 1
0.1%
0.1379 1
0.1%
0.1463 1
0.1%
0.1509 2
0.1%
0.1521 1
0.1%
ValueCountFrequency (%)
1 820
42.2%
0.9994 1
 
0.1%
0.9977 1
 
0.1%
0.9973 1
 
0.1%
0.9931 2
 
0.1%
0.9925 1
 
0.1%
0.9921 2
 
0.1%
0.992 1
 
0.1%
0.9917 1
 
0.1%
0.9916 1
 
0.1%

Outside_Global_Index
Categorical

High correlation 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size15.3 KiB
1.0
1072 
0.0
778 
0.5
 
91

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters5823
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 1072
55.2%
0.0 778
40.1%
0.5 91
 
4.7%

Length

2025-05-22T01:25:51.310987image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-22T01:25:51.527169image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1072
55.2%
0.0 778
40.1%
0.5 91
 
4.7%

Most occurring characters

ValueCountFrequency (%)
0 2719
46.7%
. 1941
33.3%
1 1072
 
18.4%
5 91
 
1.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5823
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2719
46.7%
. 1941
33.3%
1 1072
 
18.4%
5 91
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5823
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2719
46.7%
. 1941
33.3%
1 1072
 
18.4%
5 91
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5823
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2719
46.7%
. 1941
33.3%
1 1072
 
18.4%
5 91
 
1.6%

LogOfAreas
Real number (ℝ)

High correlation 

Distinct914
Distinct (%)47.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4923884
Minimum0.301
Maximum5.1837
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.3 KiB
2025-05-22T01:25:51.774797image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0.301
5-th percentile1.5911
Q11.9243
median2.2406
Q32.9149
95-th percentile4.0496
Maximum5.1837
Range4.8827
Interquartile range (IQR)0.9906

Descriptive statistics

Standard deviation0.78892985
Coefficient of variation (CV)0.31653568
Kurtosis-0.33921066
Mean2.4923884
Median Absolute Deviation (MAD)0.4017
Skewness0.74828448
Sum4837.7258
Variance0.62241031
MonotonicityNot monotonic
2025-05-22T01:25:52.079329image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.8325 19
 
1.0%
1.716 19
 
1.0%
1.7781 18
 
0.9%
1.7404 16
 
0.8%
1.2041 15
 
0.8%
1.7076 15
 
0.8%
1.8261 14
 
0.7%
1.7482 14
 
0.7%
2.0414 14
 
0.7%
1.7993 14
 
0.7%
Other values (904) 1783
91.9%
ValueCountFrequency (%)
0.301 2
 
0.1%
0.7782 2
 
0.1%
0.9031 2
 
0.1%
0.9542 2
 
0.1%
1 1
 
0.1%
1.0414 1
 
0.1%
1.0792 10
0.5%
1.1461 1
 
0.1%
1.1761 3
 
0.2%
1.2041 15
0.8%
ValueCountFrequency (%)
5.1837 1
0.1%
4.5721 1
0.1%
4.4061 1
0.1%
4.4035 1
0.1%
4.3868 1
0.1%
4.3532 1
0.1%
4.3422 1
0.1%
4.3245 1
0.1%
4.323 1
0.1%
4.32 1
0.1%

Log_X_Index
Real number (ℝ)

High correlation 

Distinct183
Distinct (%)9.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3356861
Minimum0.301
Maximum3.0741
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.3 KiB
2025-05-22T01:25:52.358714image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0.301
5-th percentile0.7782
Q11
median1.1761
Q31.5185
95-th percentile2.243
Maximum3.0741
Range2.7731
Interquartile range (IQR)0.5185

Descriptive statistics

Standard deviation0.48161161
Coefficient of variation (CV)0.36057244
Kurtosis-0.04144811
Mean1.3356861
Median Absolute Deviation (MAD)0.2219
Skewness1.0010141
Sum2592.5668
Variance0.23194974
MonotonicityNot monotonic
2025-05-22T01:25:52.620909image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9542 147
 
7.6%
1.0792 120
 
6.2%
0.9031 119
 
6.1%
1 116
 
6.0%
1.0414 112
 
5.8%
1.1139 90
 
4.6%
0.8451 81
 
4.2%
1.1461 70
 
3.6%
1.1761 58
 
3.0%
0.7782 58
 
3.0%
Other values (173) 970
50.0%
ValueCountFrequency (%)
0.301 2
 
0.1%
0.4771 3
 
0.2%
0.6021 10
 
0.5%
0.699 29
 
1.5%
0.7782 58
 
3.0%
0.8451 81
4.2%
0.9031 119
6.1%
0.9542 147
7.6%
1 116
6.0%
1.0414 112
5.8%
ValueCountFrequency (%)
3.0741 1
0.1%
2.9385 1
0.1%
2.9335 1
0.1%
2.918 1
0.1%
2.8882 1
0.1%
2.842 1
0.1%
2.8414 1
0.1%
2.8048 1
0.1%
2.7543 1
0.1%
2.7235 1
0.1%

Log_Y_Index
Real number (ℝ)

High correlation 

Distinct217
Distinct (%)11.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4032713
Minimum0
Maximum4.2587
Zeros2
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size15.3 KiB
2025-05-22T01:25:52.886932image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.699
Q11.0792
median1.3222
Q31.7324
95-th percentile2.2304
Maximum4.2587
Range4.2587
Interquartile range (IQR)0.6532

Descriptive statistics

Standard deviation0.45434516
Coefficient of variation (CV)0.32377571
Kurtosis0.38407733
Mean1.4032713
Median Absolute Deviation (MAD)0.2808
Skewness0.44510061
Sum2723.7496
Variance0.20642953
MonotonicityNot monotonic
2025-05-22T01:25:53.156798image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.0792 92
 
4.7%
1.0414 87
 
4.5%
1 78
 
4.0%
1.1461 70
 
3.6%
1.1139 66
 
3.4%
1.1761 56
 
2.9%
0.9542 56
 
2.9%
0.9031 53
 
2.7%
1.301 51
 
2.6%
1.2553 47
 
2.4%
Other values (207) 1285
66.2%
ValueCountFrequency (%)
0 2
 
0.1%
0.301 6
 
0.3%
0.4771 23
 
1.2%
0.6021 44
2.3%
0.699 31
 
1.6%
0.7782 28
 
1.4%
0.8451 31
 
1.6%
0.9031 53
2.7%
0.9542 56
2.9%
1 78
4.0%
ValueCountFrequency (%)
4.2587 1
0.1%
2.776 1
0.1%
2.6294 1
0.1%
2.6181 1
0.1%
2.6149 1
0.1%
2.5922 1
0.1%
2.5752 2
0.1%
2.5527 1
0.1%
2.5515 1
0.1%
2.5052 1
0.1%

Orientation_Index
Real number (ℝ)

High correlation  Zeros 

Distinct918
Distinct (%)47.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.083287635
Minimum-0.991
Maximum0.9917
Zeros91
Zeros (%)4.7%
Negative778
Negative (%)40.1%
Memory size15.3 KiB
2025-05-22T01:25:53.406187image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum-0.991
5-th percentile-0.6713
Q1-0.3333
median0.0952
Q30.5116
95-th percentile0.8372
Maximum0.9917
Range1.9827
Interquartile range (IQR)0.8449

Descriptive statistics

Standard deviation0.50086805
Coefficient of variation (CV)6.0137143
Kurtosis-1.0446547
Mean0.083287635
Median Absolute Deviation (MAD)0.4181
Skewness-0.15344552
Sum161.6613
Variance0.2508688
MonotonicityNot monotonic
2025-05-22T01:25:53.719593image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 91
 
4.7%
0.3333 23
 
1.2%
0.6667 21
 
1.1%
0.2 21
 
1.1%
0.5 21
 
1.1%
-0.2 21
 
1.1%
0.25 20
 
1.0%
0.1818 17
 
0.9%
-0.5 17
 
0.9%
0.1111 15
 
0.8%
Other values (908) 1674
86.2%
ValueCountFrequency (%)
-0.991 1
0.1%
-0.9739 1
0.1%
-0.9706 1
0.1%
-0.9604 1
0.1%
-0.9592 1
0.1%
-0.9559 1
0.1%
-0.9546 1
0.1%
-0.9514 1
0.1%
-0.9509 1
0.1%
-0.95 2
0.1%
ValueCountFrequency (%)
0.9917 1
0.1%
0.9607 1
0.1%
0.9578 1
0.1%
0.9552 1
0.1%
0.9481 1
0.1%
0.9467 1
0.1%
0.9463 1
0.1%
0.9431 1
0.1%
0.9419 1
0.1%
0.9388 1
0.1%

Luminosity_Index
Real number (ℝ)

High correlation 

Distinct1522
Distinct (%)78.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.13130505
Minimum-0.9989
Maximum0.6421
Zeros1
Zeros (%)0.1%
Negative1714
Negative (%)88.3%
Memory size15.3 KiB
2025-05-22T01:25:53.991852image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum-0.9989
5-th percentile-0.3479
Q1-0.195
median-0.133
Q3-0.0666
95-th percentile0.0376
Maximum0.6421
Range1.641
Interquartile range (IQR)0.1284

Descriptive statistics

Standard deviation0.14876684
Coefficient of variation (CV)-1.1329864
Kurtosis5.8067489
Mean-0.13130505
Median Absolute Deviation (MAD)0.063
Skewness0.67933872
Sum-254.8631
Variance0.022131573
MonotonicityNot monotonic
2025-05-22T01:25:54.256927image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.1851 6
 
0.3%
-0.1903 5
 
0.3%
-0.189 5
 
0.3%
-0.112 4
 
0.2%
-0.1078 4
 
0.2%
-0.122 4
 
0.2%
-0.0935 4
 
0.2%
-0.1805 4
 
0.2%
-0.0481 4
 
0.2%
-0.1797 4
 
0.2%
Other values (1512) 1897
97.7%
ValueCountFrequency (%)
-0.9989 1
0.1%
-0.885 1
0.1%
-0.8603 1
0.1%
-0.6332 1
0.1%
-0.6096 1
0.1%
-0.6017 1
0.1%
-0.5971 1
0.1%
-0.594 1
0.1%
-0.5902 1
0.1%
-0.585 1
0.1%
ValueCountFrequency (%)
0.6421 1
0.1%
0.5917 1
0.1%
0.5916 1
0.1%
0.5909 1
0.1%
0.5799 1
0.1%
0.5613 1
0.1%
0.5591 1
0.1%
0.5552 1
0.1%
0.5518 1
0.1%
0.5237 1
0.1%

SigmoidOfAreas
Real number (ℝ)

High correlation 

Distinct388
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.58542045
Minimum0.119
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.3 KiB
2025-05-22T01:25:54.528975image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0.119
5-th percentile0.1696
Q10.2482
median0.5063
Q30.9998
95-th percentile1
Maximum1
Range0.881
Interquartile range (IQR)0.7516

Descriptive statistics

Standard deviation0.33945181
Coefficient of variation (CV)0.57984275
Kurtosis-1.7076941
Mean0.58542045
Median Absolute Deviation (MAD)0.3098
Skewness0.12578852
Sum1136.3011
Variance0.11522753
MonotonicityNot monotonic
2025-05-22T01:25:54.805249image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 460
 
23.7%
0.2288 29
 
1.5%
0.1773 29
 
1.5%
0.2173 25
 
1.3%
0.2432 24
 
1.2%
0.1954 24
 
1.2%
0.2901 23
 
1.2%
0.9999 22
 
1.1%
0.2051 22
 
1.1%
0.3068 21
 
1.1%
Other values (378) 1262
65.0%
ValueCountFrequency (%)
0.119 2
 
0.1%
0.124 1
 
0.1%
0.1262 4
 
0.2%
0.1284 2
 
0.1%
0.1292 4
 
0.2%
0.1307 2
 
0.1%
0.1322 11
0.6%
0.133 1
 
0.1%
0.1353 5
0.3%
0.1361 2
 
0.1%
ValueCountFrequency (%)
1 460
23.7%
0.9999 22
 
1.1%
0.9998 16
 
0.8%
0.9997 5
 
0.3%
0.9996 4
 
0.2%
0.9995 2
 
0.1%
0.9994 2
 
0.1%
0.9993 3
 
0.2%
0.9992 2
 
0.1%
0.9991 2
 
0.1%

Pastry
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.3 KiB
0
1783 
1
 
158

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1941
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 1783
91.9%
1 158
 
8.1%

Length

2025-05-22T01:25:55.032012image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-22T01:25:55.208446image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
0 1783
91.9%
1 158
 
8.1%

Most occurring characters

ValueCountFrequency (%)
0 1783
91.9%
1 158
 
8.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1941
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1783
91.9%
1 158
 
8.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1941
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1783
91.9%
1 158
 
8.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1941
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1783
91.9%
1 158
 
8.1%

Z_Scratch
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.3 KiB
0
1751 
1
190 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1941
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1751
90.2%
1 190
 
9.8%

Length

2025-05-22T01:25:55.390788image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-22T01:25:55.568790image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
0 1751
90.2%
1 190
 
9.8%

Most occurring characters

ValueCountFrequency (%)
0 1751
90.2%
1 190
 
9.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1941
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1751
90.2%
1 190
 
9.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1941
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1751
90.2%
1 190
 
9.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1941
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1751
90.2%
1 190
 
9.8%

K_Scratch
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.3 KiB
0
1550 
1
391 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1941
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1550
79.9%
1 391
 
20.1%

Length

2025-05-22T01:25:55.765049image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-22T01:25:55.951732image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
0 1550
79.9%
1 391
 
20.1%

Most occurring characters

ValueCountFrequency (%)
0 1550
79.9%
1 391
 
20.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1941
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1550
79.9%
1 391
 
20.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1941
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1550
79.9%
1 391
 
20.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1941
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1550
79.9%
1 391
 
20.1%

Stains
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.3 KiB
0
1869 
1
 
72

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1941
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1869
96.3%
1 72
 
3.7%

Length

2025-05-22T01:25:56.137378image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-22T01:25:56.308525image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
0 1869
96.3%
1 72
 
3.7%

Most occurring characters

ValueCountFrequency (%)
0 1869
96.3%
1 72
 
3.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1941
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1869
96.3%
1 72
 
3.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1941
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1869
96.3%
1 72
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1941
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1869
96.3%
1 72
 
3.7%

Dirtiness
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.3 KiB
0
1886 
1
 
55

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1941
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1886
97.2%
1 55
 
2.8%

Length

2025-05-22T01:25:56.495343image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-22T01:25:56.664867image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
0 1886
97.2%
1 55
 
2.8%

Most occurring characters

ValueCountFrequency (%)
0 1886
97.2%
1 55
 
2.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1941
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1886
97.2%
1 55
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1941
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1886
97.2%
1 55
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1941
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1886
97.2%
1 55
 
2.8%

Bumps
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.3 KiB
0
1539 
1
402 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1941
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1539
79.3%
1 402
 
20.7%

Length

2025-05-22T01:25:56.864953image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-22T01:25:57.038169image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
0 1539
79.3%
1 402
 
20.7%

Most occurring characters

ValueCountFrequency (%)
0 1539
79.3%
1 402
 
20.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1941
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1539
79.3%
1 402
 
20.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1941
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1539
79.3%
1 402
 
20.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1941
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1539
79.3%
1 402
 
20.7%

Other_Faults
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.3 KiB
0
1268 
1
673 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1941
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1268
65.3%
1 673
34.7%

Length

2025-05-22T01:25:57.223714image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-22T01:25:57.396679image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
0 1268
65.3%
1 673
34.7%

Most occurring characters

ValueCountFrequency (%)
0 1268
65.3%
1 673
34.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1941
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1268
65.3%
1 673
34.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1941
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1268
65.3%
1 673
34.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1941
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1268
65.3%
1 673
34.7%

Interactions

2025-05-22T01:25:24.071833image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:04.764522image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:11.900867image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:25.862439image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:33.882048image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:41.209513image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:50.455340image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:01.009049image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:14.415739image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:25.989704image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:33.579106image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:41.030191image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:49.614168image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:59.517776image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:12.040475image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:21.285445image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:28.562164image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:36.187177image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:43.395673image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:50.679058image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:57.032750image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:25:03.514795image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:25:09.800230image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:25:16.688736image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:25:24.341882image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:05.119069image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:12.316792image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:26.427600image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:34.230509image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:41.591411image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:50.815638image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:01.332541image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:14.798768image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:26.250770image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:33.893427image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:41.415647image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:49.886442image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:59.904356image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:12.383444image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:21.592166image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:29.064288image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:36.616162image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:43.663410image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:50.938635image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:57.294573image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:25:03.783144image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:25:10.097241image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:25:16.983873image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:25:24.613976image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:05.380014image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:12.721805image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:26.777918image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:34.610709image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:41.934430image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:51.393743image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:01.708328image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:15.287801image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:26.525106image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:34.227621image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:41.743743image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:50.175413image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:00.236124image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:12.673783image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:21.858528image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:29.363511image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:36.968980image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:43.948899image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:51.213993image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:57.580592image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:25:04.033305image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:25:10.356831image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:25:17.310705image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:25:24.897751image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:05.660307image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:13.028849image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:27.343810image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:34.882587image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:42.234647image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:51.760094image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:02.171865image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:16.151481image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:26.780352image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:34.485511image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:41.991962image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:50.449662image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:00.797394image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:12.978231image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:22.124315image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:29.671817image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:37.278821image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:44.216014image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:51.468269image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:57.820582image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:25:04.296857image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:25:10.636800image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:25:17.617911image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:25:25.143316image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:05.989786image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:15.170242image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:27.634455image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:35.179261image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:42.536242image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:52.090212image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:02.702188image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:16.618141image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:27.049686image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:34.778245image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:42.266900image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:50.888047image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:01.184426image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:13.640915image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:22.384330image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:29.936841image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:37.598510image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:44.513526image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:51.742468image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:58.095409image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:25:04.568016image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:25:10.910491image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:25:17.930772image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:25:25.556437image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:06.228941image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:17.212498image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:27.948502image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:35.486097image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:42.897693image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:52.828193image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:03.488775image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:17.116683image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:27.306771image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:35.052346image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:42.627490image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:51.233656image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:01.849799image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:14.185412image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:22.693841image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:30.214604image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:37.922557image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:44.843978image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:52.005163image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:58.360669image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:25:04.882922image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:25:11.213906image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:25:18.208539image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:25:25.952066image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:06.488817image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:17.852641image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:28.261228image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:35.747483image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:43.444676image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:53.295740image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:03.749002image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:17.583939image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:27.582936image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:35.490088image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:43.115888image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:51.575108image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:02.231339image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:14.624551image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:23.013398image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:30.509838image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:38.298768image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:45.959432image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:52.283464image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:58.696165image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:25:05.123107image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:25:11.484489image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:25:18.465863image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:25:26.371618image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:06.750791image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:18.434448image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:28.646086image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:36.034074image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:43.936749image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:53.687878image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:04.089248image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:18.213799image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:27.823916image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:35.902309image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:43.525001image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:51.959421image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:02.718802image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:15.051221image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:23.303831image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:30.936521image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:38.618064image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:46.207693image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:52.538044image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:58.985976image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:25:05.384917image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:25:11.765289image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:25:18.793157image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:25:26.721783image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:06.992886image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:19.007566image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:29.114935image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:36.325188image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:44.222830image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:54.010424image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:04.384618image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:18.721525image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:28.089566image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:36.229870image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:44.066382image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:52.627350image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:03.135955image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:15.341750image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:23.590164image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:31.466903image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:38.906463image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:46.480042image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:52.808366image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:59.248057image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:25:05.637444image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:25:12.051121image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:25:19.130864image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:25:27.069277image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:07.251952image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:19.793362image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:29.449625image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:36.627609image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:44.534230image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:54.377301image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:04.738035image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:19.209584image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:28.363122image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:36.475391image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:44.351975image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:53.149237image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:03.679105image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:15.711459image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:23.858915image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:31.855693image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:39.212961image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:46.744346image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:53.090855image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:59.535150image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:25:05.923232image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:25:12.332288image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:25:19.425568image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:25:27.412135image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:07.508154image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:20.218402image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:29.725955image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:36.952788image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:44.932945image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:54.655620image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:05.046635image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:19.770790image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:28.583992image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:36.761768image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:44.621365image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:53.451898image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:04.287611image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:15.961938image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:24.128372image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:32.244490image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:39.464875image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:46.999464image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:53.356657image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:59.791698image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:25:06.163358image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:25:12.610247image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:25:19.672557image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:25:27.768772image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:07.772375image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:20.636317image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:30.027487image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:37.253221image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:45.230090image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:55.179229image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:05.411035image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:20.447963image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:29.043445image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:37.189000image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:44.919429image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:53.733783image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:04.685635image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:16.246464image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:24.475578image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:32.616974image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:39.746062image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:47.267635image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:53.614336image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:25:00.085097image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:25:06.422603image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:25:12.886655image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:25:19.938843image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:25:28.112443image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:08.026160image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:21.150617image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:30.298837image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:37.552027image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:45.515032image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:55.578982image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:07.373234image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:21.062553image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:29.487046image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:37.455067image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:45.394190image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:54.019624image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:05.322245image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:16.545456image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:24.780823image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:32.937990image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:40.016751image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:47.536301image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:53.921755image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:25:00.335898image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:25:06.665750image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:25:13.160843image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:25:20.215956image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:25:28.454481image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:08.260691image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:21.566336image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:30.560499image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:37.820781image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:45.835670image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:56.151036image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:08.279069image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:21.644017image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:29.943678image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:37.688302image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:46.341125image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:54.335377image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:05.850110image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:16.777404image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:25.073077image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:33.195519image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:40.286496image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:47.793531image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:54.188290image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:25:00.624512image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:25:06.930047image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:25:13.460893image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:25:20.454465image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:25:29.011124image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:08.505295image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:22.135682image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:30.916869image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:38.082131image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:46.143418image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:56.458040image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:08.922644image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:22.131329image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:30.197239image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:38.154715image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:46.812745image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:54.791639image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:06.251781image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:17.805198image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:25.326181image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:33.486855image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:40.545713image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:48.037456image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:54.429668image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:25:00.904752image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:25:07.176781image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:25:13.750677image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:25:20.708064image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:25:29.535914image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:08.977419image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:22.517680image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:31.202695image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:38.407882image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:46.437441image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:56.951449image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:09.413487image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:22.599314image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:30.474733image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:38.526507image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:47.122461image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:55.256027image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:06.886310image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:18.155868image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:25.631248image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:33.748581image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:40.860188image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:48.366345image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:54.689647image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:25:01.176094image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:25:07.448450image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:25:14.041087image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:25:20.990647image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:25:30.014592image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:09.280175image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:23.050807image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:31.526491image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:38.688999image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:46.869054image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:57.292870image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:09.954569image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:22.978030image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:30.748059image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:38.850417image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:47.349783image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:55.677271image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:07.740722image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:18.456664image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:25.903412image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:34.025877image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:41.165983image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:48.626709image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:54.946157image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:25:01.415845image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:25:07.684966image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:25:14.305560image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:25:21.248649image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:25:30.387447image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:09.590650image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:23.444490image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:31.792036image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:38.954874image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:47.629101image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:57.780620image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:10.302618image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:23.400559image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:31.028328image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:39.152645image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:47.694216image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:55.992354image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:07.991820image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:18.868797image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:26.281428image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:34.265705image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:41.486241image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:48.874520image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:55.189944image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:25:01.679895image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:25:07.935272image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:25:14.581750image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:25:21.500380image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:25:30.697183image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:09.904341image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:23.821413image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:32.060957image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:39.247560image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:48.063852image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:58.256642image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:10.712972image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:23.827782image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:31.266983image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:39.427562image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:47.939186image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:56.315581image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:08.321077image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:19.238995image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:26.676790image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:34.556834image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:41.819914image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:49.108982image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:55.433695image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:25:01.947457image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:25:08.164422image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:25:14.848908image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:25:21.752546image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:25:30.967226image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:10.213527image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:24.134615image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:32.353618image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:39.511735image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:48.447948image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:58.607435image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:11.408552image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:24.176566image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:31.537293image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:39.643984image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:48.226009image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:56.798607image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:09.229466image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:19.635526image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:26.994251image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:34.791326image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:42.086625image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:49.342749image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:55.671914image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:25:02.208566image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:25:08.410627image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:25:15.130928image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:25:22.784993image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:25:31.218002image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:10.527158image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:24.528348image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:32.621497image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:39.792174image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:48.868911image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:59.012938image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:11.891388image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:24.587857image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:31.776132image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:39.904037image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:48.490362image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:57.192084image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:09.669232image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:20.030713image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:27.300757image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:35.043343image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:42.331991image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:49.598637image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:55.935850image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:25:02.467110image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:25:08.731007image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:25:15.387346image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:25:23.024812image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:25:31.461159image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:10.831366image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:24.853272image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:32.965528image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:40.056302image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:49.223079image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:59.382844image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:12.539368image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:24.898571image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:32.113771image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:40.151796image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:48.773629image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:58.276533image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:10.086077image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:20.392186image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:27.563314image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:35.290599image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:42.585862image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:49.847415image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:56.174031image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:25:02.710514image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:25:09.030244image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:25:15.658991image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:25:23.281377image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:25:31.785846image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:11.181050image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:25.187322image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:33.288692image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:40.376606image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:49.731547image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:59.942997image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:13.348059image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:25.206718image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:32.387713image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:40.486656image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:49.061193image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:58.765729image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:10.839496image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:20.714798image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:27.998532image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:35.586235image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:42.870213image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:50.140531image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:56.473533image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:25:02.985786image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:25:09.291359image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:25:15.991647image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:25:23.555814image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:25:32.061327image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:11.556684image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:25.495191image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:33.608170image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:40.982359image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:22:50.147366image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:00.507653image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:13.978697image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:25.495315image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:32.733365image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:40.769152image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:49.353963image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:23:59.135916image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:11.494978image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:21.010164image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:28.297711image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:35.840483image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:43.152116image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:50.423128image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:24:56.780841image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:25:03.249214image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:25:09.562496image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:25:16.310630image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-22T01:25:23.827579image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Correlations

2025-05-22T01:25:57.648677image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
BumpsDirtinessEdges_IndexEdges_X_IndexEdges_Y_IndexEmpty_IndexK_ScratchLength_of_ConveyerLogOfAreasLog_X_IndexLog_Y_IndexLuminosity_IndexMaximum_of_LuminosityMinimum_of_LuminosityOrientation_IndexOther_FaultsOutside_Global_IndexOutside_X_IndexPastryPixels_AreasSigmoidOfAreasSquare_IndexStainsSteel_Plate_ThicknessSum_of_LuminosityTypeOfSteel_A300TypeOfSteel_A400X_MaximumX_MinimumX_PerimeterY_MaximumY_MinimumY_PerimeterZ_Scratch
Bumps1.0000.0800.2780.2410.2250.2580.2540.2960.2890.2470.3300.1210.1080.2790.2860.3700.1040.2170.1480.0660.2510.2860.0940.1640.1170.3040.3040.2560.2830.0000.1940.1940.0000.165
Dirtiness0.0801.0000.3380.2610.0780.0300.0790.0730.1270.1450.0510.0840.0620.1270.3030.1190.1060.0420.0390.0000.0710.2800.0110.2770.0000.0760.0760.3190.3290.0000.2560.2560.0000.046
Edges_Index0.2780.3381.0000.2730.266-0.1640.4650.125-0.386-0.281-0.4200.2310.1530.398-0.0290.2050.116-0.2830.063-0.386-0.3670.1700.2130.174-0.3520.1660.1660.3910.459-0.352-0.027-0.027-0.4200.193
Edges_X_Index0.2410.2610.2731.0000.186-0.4150.3130.057-0.536-0.202-0.7300.0720.0070.206-0.5370.1580.329-0.2140.164-0.536-0.5560.2380.262-0.008-0.5260.2060.2060.1650.192-0.5780.0360.036-0.6850.172
Edges_Y_Index0.2250.0780.2660.1861.000-0.5720.7150.127-0.551-0.808-0.350-0.161-0.2250.1880.5740.2550.412-0.8130.241-0.551-0.5970.0780.1270.327-0.5740.2820.2820.2360.368-0.746-0.069-0.069-0.5480.149
Empty_Index0.2580.030-0.164-0.415-0.5721.0000.267-0.1660.3540.5110.3710.1700.1450.038-0.1420.0530.1740.5300.1920.3540.492-0.0750.000-0.0690.3680.1410.141-0.162-0.2150.579-0.004-0.0040.4710.145
K_Scratch0.2540.0790.4650.3130.7150.2671.0000.5830.8100.8290.5890.2250.2440.7020.5550.3640.2950.8190.1450.2760.5600.3730.0920.5270.4610.4060.4060.6220.5150.0930.1980.1980.0000.162
Length_of_Conveyer0.2960.0730.1250.0570.127-0.1660.5831.000-0.056-0.064-0.064-0.0370.052-0.0990.0720.2370.107-0.1820.195-0.056-0.0920.1570.1410.194-0.0670.4340.4340.2550.246-0.099-0.009-0.009-0.0960.249
LogOfAreas0.2890.127-0.386-0.536-0.5510.3540.810-0.0561.0000.7900.909-0.2250.004-0.5840.0060.2990.2880.7840.1691.0000.977-0.2470.851-0.2850.9850.4200.420-0.269-0.4160.8960.0450.0450.9490.141
Log_X_Index0.2470.145-0.281-0.202-0.8080.5110.829-0.0640.7901.0000.549-0.0550.127-0.429-0.5010.3050.3310.9890.2190.7900.819-0.0600.412-0.3330.7950.3700.370-0.245-0.3920.8890.1030.1030.6860.167
Log_Y_Index0.3300.051-0.420-0.730-0.3500.3710.589-0.0640.9090.5491.000-0.250-0.051-0.5490.3310.2100.3200.5510.1700.9090.899-0.3150.600-0.1860.8900.3560.356-0.263-0.3830.7790.0010.0010.9570.061
Luminosity_Index0.1210.0840.2310.072-0.1610.1700.225-0.037-0.225-0.055-0.2501.0000.8530.717-0.1820.1180.182-0.0360.100-0.225-0.2030.1240.334-0.158-0.1050.2140.214-0.0180.008-0.085-0.077-0.077-0.1810.167
Maximum_of_Luminosity0.1080.0620.1530.007-0.2250.1450.2440.0520.0040.127-0.0510.8531.0000.415-0.1850.1440.1330.1310.0390.004-0.0030.1110.095-0.1610.1110.3110.311-0.059-0.0780.094-0.062-0.0620.0080.235
Minimum_of_Luminosity0.2790.1270.3980.2060.1880.0380.702-0.099-0.584-0.429-0.5490.7170.4151.0000.0050.2580.207-0.4020.187-0.584-0.5160.0560.2770.127-0.4960.3800.3800.1350.284-0.447-0.113-0.113-0.5100.127
Orientation_Index0.2860.303-0.029-0.5370.574-0.1420.5550.0720.006-0.5010.331-0.182-0.1850.0051.0000.2380.784-0.4940.3860.006-0.018-0.2260.2530.317-0.0140.2660.2660.0490.148-0.192-0.130-0.1300.1460.143
Other_Faults0.3700.1190.2050.1580.2550.0530.3640.2370.2990.3050.2100.1180.1440.2580.2381.0000.0210.2740.2140.0900.1710.1650.1380.3220.1600.0000.0000.2360.2070.0000.2060.2060.0000.237
Outside_Global_Index0.1040.1060.1160.3290.4120.1740.2950.1070.2880.3310.3200.1820.1330.2070.7840.0211.0000.2450.2470.0260.2100.3990.2040.1700.0540.0700.0700.2470.1630.0390.0870.0870.0000.079
Outside_X_Index0.2170.042-0.283-0.214-0.8130.5300.819-0.1820.7840.9890.551-0.0360.131-0.402-0.4940.2740.2451.0000.1170.7840.816-0.0730.060-0.3620.7910.3320.332-0.273-0.4160.8880.1020.1020.6890.128
Pastry0.1480.0390.0630.1640.2410.1920.1450.1950.1690.2190.1700.1000.0390.1870.3860.2140.2470.1171.0000.0210.1190.2490.0480.2020.0460.0480.0480.2150.1900.0000.1210.1210.0000.092
Pixels_Areas0.0660.000-0.386-0.536-0.5510.3540.276-0.0561.0000.7900.909-0.2250.004-0.5840.0060.0900.0260.7840.0211.0000.977-0.2470.000-0.2850.9850.1010.101-0.269-0.4160.8960.0450.0450.9490.030
SigmoidOfAreas0.2510.071-0.367-0.556-0.5970.4920.560-0.0920.9770.8190.899-0.203-0.003-0.516-0.0180.1710.2100.8160.1190.9771.000-0.2810.421-0.2540.9650.3310.331-0.274-0.4030.9280.0380.0380.9560.064
Square_Index0.2860.2800.1700.2380.078-0.0750.3730.157-0.247-0.060-0.3150.1240.1110.056-0.2260.1650.399-0.0730.249-0.247-0.2811.0000.105-0.102-0.2470.2310.2310.0810.086-0.2590.0180.018-0.3420.078
Stains0.0940.0110.2130.2620.1270.0000.0920.1410.8510.4120.6000.3340.0950.2770.2530.1380.2040.0600.0480.0000.4210.1051.0000.1930.0160.1500.1500.1690.1940.0000.0630.0630.0000.056
Steel_Plate_Thickness0.1640.2770.174-0.0080.327-0.0690.5270.194-0.285-0.333-0.186-0.158-0.1610.1270.3170.3220.170-0.3620.202-0.285-0.254-0.1020.1931.000-0.3090.7240.7240.1440.251-0.278-0.196-0.196-0.2360.428
Sum_of_Luminosity0.1170.000-0.352-0.526-0.5740.3680.461-0.0670.9850.7950.890-0.1050.111-0.496-0.0140.1600.0540.7910.0460.9850.965-0.2470.016-0.3091.0000.1810.181-0.276-0.4200.8950.0240.0240.9390.067
TypeOfSteel_A3000.3040.0760.1660.2060.2820.1410.4060.4340.4200.3700.3560.2140.3110.3800.2660.0000.0700.3320.0480.1010.3310.2310.1500.7240.1811.0000.9990.3150.1990.0040.1960.1960.0000.337
TypeOfSteel_A4000.3040.0760.1660.2060.2820.1410.4060.4340.4200.3700.3560.2140.3110.3800.2660.0000.0700.3320.0480.1010.3310.2310.1500.7240.1810.9991.0000.3150.1990.0040.1960.1960.0000.337
X_Maximum0.2560.3190.3910.1650.236-0.1620.6220.255-0.269-0.245-0.263-0.018-0.0590.1350.0490.2360.247-0.2730.215-0.269-0.2740.0810.1690.144-0.2760.3150.3151.0000.949-0.2680.0320.032-0.2760.359
X_Minimum0.2830.3290.4590.1920.368-0.2150.5150.246-0.416-0.392-0.3830.008-0.0780.2840.1480.2070.163-0.4160.190-0.416-0.4030.0860.1940.251-0.4200.1990.1990.9491.000-0.409-0.004-0.004-0.4120.250
X_Perimeter0.0000.000-0.352-0.578-0.7460.5790.093-0.0990.8960.8890.779-0.0850.094-0.447-0.1920.0000.0390.8880.0000.8960.928-0.2590.000-0.2780.8950.0040.004-0.268-0.4091.0000.0760.0760.8810.000
Y_Maximum0.1940.256-0.0270.036-0.069-0.0040.198-0.0090.0450.1030.001-0.077-0.062-0.113-0.1300.2060.0870.1020.1210.0450.0380.0180.063-0.1960.0240.1960.1960.032-0.0040.0761.0001.0000.0190.106
Y_Minimum0.1940.256-0.0270.036-0.069-0.0040.198-0.0090.0450.1030.001-0.077-0.062-0.113-0.1300.2060.0870.1020.1210.0450.0380.0180.063-0.1960.0240.1960.1960.032-0.0040.0761.0001.0000.0190.106
Y_Perimeter0.0000.000-0.420-0.685-0.5480.4710.000-0.0960.9490.6860.957-0.1810.008-0.5100.1460.0000.0000.6890.0000.9490.956-0.3420.000-0.2360.9390.0000.000-0.276-0.4120.8810.0190.0191.0000.000
Z_Scratch0.1650.0460.1930.1720.1490.1450.1620.2490.1410.1670.0610.1670.2350.1270.1430.2370.0790.1280.0920.0300.0640.0780.0560.4280.0670.3370.3370.3590.2500.0000.1060.1060.0001.000

Missing values

2025-05-22T01:25:32.628780image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-05-22T01:25:34.213166image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

X_MinimumX_MaximumY_MinimumY_MaximumPixels_AreasX_PerimeterY_PerimeterSum_of_LuminosityMinimum_of_LuminosityMaximum_of_LuminosityLength_of_ConveyerTypeOfSteel_A300TypeOfSteel_A400Steel_Plate_ThicknessEdges_IndexEmpty_IndexSquare_IndexOutside_X_IndexEdges_X_IndexEdges_Y_IndexOutside_Global_IndexLogOfAreasLog_X_IndexLog_Y_IndexOrientation_IndexLuminosity_IndexSigmoidOfAreasPastryZ_ScratchK_ScratchStainsDirtinessBumpsOther_Faults
0425027090027094426717442422076108168710800.04980.24150.18180.00470.47061.00001.02.42650.90311.64350.8182-0.29130.58221000000
16456512538079253810810810301139784123168710800.76470.37930.20690.00360.60000.96671.02.03340.77821.46240.7931-0.17560.29841000000
282983515539131553931718197972991251623101000.97100.34260.33330.00370.75000.94741.01.85130.77821.25530.6667-0.12280.21501000000
3853860369370369415176134518996991261353012900.72870.44130.15560.00520.53851.00001.02.24550.84511.65320.8444-0.15680.52121000000
412891306498078498335240960260246930371261353011850.06950.44860.06620.01260.28330.98851.03.38181.23052.40990.9338-0.19921.00001000000
543044110025010033763020876235764127138701400.62000.34170.12640.00790.55001.00001.02.79931.04141.93950.8736-0.22670.98741000000
641344613846813888390522304321481991231991687011500.48960.33900.07950.01960.14350.96071.03.95671.51852.61810.92050.27911.00001000000
71902002109362109561321120200071241721687011500.22530.34000.50000.00590.90911.00001.02.12061.00001.30100.50000.18410.33591000000
8330343429227429253264152629748531481687011500.39120.21890.50000.00770.86671.00001.02.42161.11391.41500.5000-0.11970.55931000000
97490779144779308150646167180215531431687011500.08770.42610.09760.00950.34780.98201.03.17781.20412.21480.9024-0.06511.00001000000
X_MinimumX_MaximumY_MinimumY_MaximumPixels_AreasX_PerimeterY_PerimeterSum_of_LuminosityMinimum_of_LuminosityMaximum_of_LuminosityLength_of_ConveyerTypeOfSteel_A300TypeOfSteel_A400Steel_Plate_ThicknessEdges_IndexEmpty_IndexSquare_IndexOutside_X_IndexEdges_X_IndexEdges_Y_IndexOutside_Global_IndexLogOfAreasLog_X_IndexLog_Y_IndexOrientation_IndexLuminosity_IndexSigmoidOfAreasPastryZ_ScratchK_ScratchStainsDirtinessBumpsOther_Faults
1931523567266325266337209673026833119141136001400.76910.60420.27270.03230.65670.40000.02.32011.64351.0792-0.72730.00300.81830000001
1932239269276029276047299512237820116140136001400.35150.44630.60000.02210.58820.81820.02.47571.47711.2553-0.4000-0.01180.82990000001
19333674222896472896653551165846882123143136001400.53970.64140.32730.04040.47410.31030.02.55021.74041.2553-0.67270.03170.98990000001
1934137170301492301511304592635778111126136001400.20150.51520.57580.02430.55930.73080.02.48291.51851.2787-0.4242-0.08050.89710000001
1935238287315114315142671913986424119143136001400.35000.51090.57140.03600.53850.71790.02.82671.69021.4472-0.42860.00620.99920000001
1936249277325780325796273542235033119141136001400.36620.39060.57140.02060.51850.72730.02.43621.44721.2041-0.42860.00260.72540000001
1937144175340581340598287442434599112133136001400.21180.45540.54840.02280.70460.70830.02.45791.49141.2305-0.4516-0.05820.81730000001
1938145174386779386794292402237572120140136001400.21320.32870.51720.02130.72500.68180.02.46541.46241.1761-0.48280.00520.70790000001
1939137170422497422528419974752715117140136001400.20150.59040.93940.02430.34020.65960.02.62221.51851.4914-0.0606-0.01710.99190000001
1940126112818795187967103262211682101133136010800.11620.67810.80000.01470.76920.72730.02.01281.30101.2041-0.2000-0.11390.52960000001